Predicting synthetic rating curve adjustment factors with explainable machine learning for enhancing the United States operational flood inundation mapping framework

• Synthetic Rating Curve adjustment factors for NHDPlus river network are predicted using explainable ML models. • The predicted flood inundation maps for Hurricane Matthew show a mean improvement of 18–20% after SRC adjustment. • A 14% mean improvement is found in buildings hit by the predicted flood maps after the SRC adjustment. The increasing threats of global flood risk mandate rapid and accurate high-resolution flood modeling strategies over large scales. In the United States, the National Oceanic and Atmospheric Administration (NOAA) Office of Water Prediction (OWP) has operationalised a Flood Inundation Mapping (FIM) framework utilising the Height Above Nearest Drainage (HAND)-Synthetic Rating Curve (SRC) approach. It translates streamflow into stage and subsequently maps the inundation over the floodplain. It is a low-fidelity FIM framework, suitable for large-scale applications with much less computational effort. The SRCs are calculated for each river segment using Manning’s equation; however, uncertainty in Manning’s parameters and missing bathymetry impart bias in SRC calculation, and thus in FIM. An SRC adjustment factor (λ src ), introduced by OWP, calibrates SRCs against USGS rating curves, HEC-RAS 1D rating curves, and National Weather Service (NWS)-Categorical Flood Inundation Mapping (CatFIM) locations. Adjusted SRCs improve the FIM predictions but are limited to locations with the above data sources. In this paper, we develop machine learning models to predict the λ src over the entire United States river network. Results show that the eXtreme Gradient Boosting model yielded the strongest predictability, with an R 2 of 0.70. The impact of λ src on FIM predictions is evaluated for Hurricane Matthew in North Carolina and synthetic flood events in 15 watersheds. For Hurricane Matthew flooding, the mean percentage improvements in Critical Success Index (CSI), Probability of Detection (POD), and F1 Score are 17.5%, 20% and 12.5%, while for synthetic events, the improvements are 2.59%, 4.93%, and 3.03%, respectively.